Overview

Dataset statistics

Number of variables26
Number of observations13096
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory208.0 B

Variable types

Numeric18
Categorical8

Alerts

OS_3 has constant value "100.0"Constant
sensor_1 has constant value "518.67"Constant
sensor_5 has constant value "14.62"Constant
sensor_10 has constant value "1.3"Constant
sensor_16 has constant value "0.03"Constant
sensor_18 has constant value "2388"Constant
sensor_19 has constant value "100.0"Constant
sensor_2 is highly overall correlated with sensor_4 and 8 other fieldsHigh correlation
sensor_4 is highly overall correlated with time_cycles and 10 other fieldsHigh correlation
sensor_7 is highly overall correlated with sensor_2 and 9 other fieldsHigh correlation
sensor_8 is highly overall correlated with sensor_2 and 8 other fieldsHigh correlation
sensor_9 is highly overall correlated with sensor_14High correlation
sensor_11 is highly overall correlated with time_cycles and 10 other fieldsHigh correlation
sensor_12 is highly overall correlated with sensor_2 and 9 other fieldsHigh correlation
sensor_13 is highly overall correlated with sensor_2 and 8 other fieldsHigh correlation
sensor_14 is highly overall correlated with sensor_9High correlation
sensor_15 is highly overall correlated with sensor_2 and 8 other fieldsHigh correlation
sensor_17 is highly overall correlated with sensor_4 and 3 other fieldsHigh correlation
sensor_20 is highly overall correlated with sensor_2 and 8 other fieldsHigh correlation
sensor_21 is highly overall correlated with sensor_2 and 8 other fieldsHigh correlation
sensor_3 is highly overall correlated with sensor_4 and 3 other fieldsHigh correlation
time_cycles is highly overall correlated with sensor_4 and 1 other fieldsHigh correlation
OS_1 has 227 (1.7%) zerosZeros
OS_2 has 1272 (9.7%) zerosZeros

Reproduction

Analysis started2022-12-23 11:04:21.120572
Analysis finished2022-12-23 11:06:28.568960
Duration2 minutes and 7.45 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Unit_number
Real number (ℝ)

Distinct100
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.543907
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.4 KiB
2022-12-23T16:36:30.297887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q128
median52
Q376
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)48

Descriptive statistics

Standard deviation28.289423
Coefficient of variation (CV)0.54884127
Kurtosis-1.1713121
Mean51.543907
Median Absolute Deviation (MAD)24
Skewness0.0017422866
Sum675019
Variance800.29147
MonotonicityIncreasing
2022-12-23T16:36:30.596738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 303
 
2.3%
93 244
 
1.9%
91 234
 
1.8%
62 232
 
1.8%
12 217
 
1.7%
81 213
 
1.6%
76 205
 
1.6%
34 203
 
1.6%
100 198
 
1.5%
35 198
 
1.5%
Other values (90) 10849
82.8%
ValueCountFrequency (%)
1 31
 
0.2%
2 49
 
0.4%
3 126
1.0%
4 106
0.8%
5 98
0.7%
6 105
0.8%
7 160
1.2%
8 166
1.3%
9 55
 
0.4%
10 192
1.5%
ValueCountFrequency (%)
100 198
1.5%
99 97
 
0.7%
98 121
0.9%
97 134
1.0%
96 97
 
0.7%
95 89
 
0.7%
94 133
1.0%
93 244
1.9%
92 150
1.1%
91 234
1.8%

time_cycles
Real number (ℝ)

Distinct303
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.836515
Minimum1
Maximum303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.4 KiB
2022-12-23T16:36:30.943202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q133
median69
Q3113
95-th percentile173
Maximum303
Range302
Interquartile range (IQR)80

Descriptive statistics

Standard deviation53.057749
Coefficient of variation (CV)0.6905278
Kurtosis0.20570471
Mean76.836515
Median Absolute Deviation (MAD)39
Skewness0.72433178
Sum1006251
Variance2815.1248
MonotonicityNot monotonic
2022-12-23T16:36:31.232042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 100
 
0.8%
17 100
 
0.8%
31 100
 
0.8%
30 100
 
0.8%
29 100
 
0.8%
28 100
 
0.8%
27 100
 
0.8%
26 100
 
0.8%
25 100
 
0.8%
24 100
 
0.8%
Other values (293) 12096
92.4%
ValueCountFrequency (%)
1 100
0.8%
2 100
0.8%
3 100
0.8%
4 100
0.8%
5 100
0.8%
6 100
0.8%
7 100
0.8%
8 100
0.8%
9 100
0.8%
10 100
0.8%
ValueCountFrequency (%)
303 1
< 0.1%
302 1
< 0.1%
301 1
< 0.1%
300 1
< 0.1%
299 1
< 0.1%
298 1
< 0.1%
297 1
< 0.1%
296 1
< 0.1%
295 1
< 0.1%
294 1
< 0.1%

OS_1
Real number (ℝ)

Distinct150
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.1178986 × 10-5
Minimum-0.0082
Maximum0.0078
Zeros227
Zeros (%)1.7%
Negative6470
Negative (%)49.4%
Memory size102.4 KiB
2022-12-23T16:36:31.779737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.0082
5-th percentile-0.0036
Q1-0.0015
median0
Q30.0015
95-th percentile0.0036
Maximum0.0078
Range0.016
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.0022026851
Coefficient of variation (CV)-197.038
Kurtosis0.0088020963
Mean-1.1178986 × 10-5
Median Absolute Deviation (MAD)0.0015
Skewness-0.0022824912
Sum-0.1464
Variance4.8518216 × 10-6
MonotonicityNot monotonic
2022-12-23T16:36:32.066577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0003 249
 
1.9%
-0.0005 245
 
1.9%
-0.0006 244
 
1.9%
0.0001 244
 
1.9%
0.0004 239
 
1.8%
-0.001 237
 
1.8%
0.0005 235
 
1.8%
0.0006 234
 
1.8%
-0.0002 234
 
1.8%
0.0002 233
 
1.8%
Other values (140) 10702
81.7%
ValueCountFrequency (%)
-0.0082 1
 
< 0.1%
-0.0079 1
 
< 0.1%
-0.0077 3
< 0.1%
-0.0074 1
 
< 0.1%
-0.0073 2
< 0.1%
-0.0071 4
< 0.1%
-0.007 1
 
< 0.1%
-0.0069 3
< 0.1%
-0.0068 2
< 0.1%
-0.0067 2
< 0.1%
ValueCountFrequency (%)
0.0078 1
 
< 0.1%
0.0077 1
 
< 0.1%
0.0076 4
< 0.1%
0.0075 1
 
< 0.1%
0.0072 2
 
< 0.1%
0.007 1
 
< 0.1%
0.0069 1
 
< 0.1%
0.0068 2
 
< 0.1%
0.0066 2
 
< 0.1%
0.0064 5
< 0.1%

OS_2
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2379352 × 10-6
Minimum-0.0006
Maximum0.0007
Zeros1272
Zeros (%)9.7%
Negative5869
Negative (%)44.8%
Memory size102.4 KiB
2022-12-23T16:36:32.387794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.0006
5-th percentile-0.0004
Q1-0.0002
median0
Q30.0003
95-th percentile0.0005
Maximum0.0007
Range0.0013
Interquartile range (IQR)0.0005

Descriptive statistics

Standard deviation0.00029403057
Coefficient of variation (CV)69.380618
Kurtosis-1.1315659
Mean4.2379352 × 10-6
Median Absolute Deviation (MAD)0.0003
Skewness0.016358468
Sum0.0555
Variance8.6453974 × 10-8
MonotonicityNot monotonic
2022-12-23T16:36:32.619683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
-0.0002 1359
10.4%
-0.0003 1354
10.3%
0.0003 1321
10.1%
0.0001 1320
10.1%
0.0004 1290
9.9%
0.0002 1280
9.8%
-0.0001 1273
9.7%
0 1272
9.7%
-0.0004 1245
9.5%
0.0005 681
5.2%
Other values (4) 701
5.4%
ValueCountFrequency (%)
-0.0006 13
 
0.1%
-0.0005 625
4.8%
-0.0004 1245
9.5%
-0.0003 1354
10.3%
-0.0002 1359
10.4%
-0.0001 1273
9.7%
0 1272
9.7%
0.0001 1320
10.1%
0.0002 1280
9.8%
0.0003 1321
10.1%
ValueCountFrequency (%)
0.0007 5
 
< 0.1%
0.0006 58
 
0.4%
0.0005 681
5.2%
0.0004 1290
9.9%
0.0003 1321
10.1%
0.0002 1280
9.8%
0.0001 1320
10.1%
0 1272
9.7%
-0.0001 1273
9.7%
-0.0002 1359
10.4%

OS_3
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size102.4 KiB
100.0
13096 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters65480
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row100.0
4th row100.0
5th row100.0

Common Values

ValueCountFrequency (%)
100.0 13096
100.0%

Length

2022-12-23T16:36:32.852534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-23T16:36:35.202857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
100.0 13096
100.0%

Most occurring characters

ValueCountFrequency (%)
0 39288
60.0%
1 13096
 
20.0%
. 13096
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52384
80.0%
Other Punctuation 13096
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39288
75.0%
1 13096
 
25.0%
Other Punctuation
ValueCountFrequency (%)
. 13096
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65480
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39288
60.0%
1 13096
 
20.0%
. 13096
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39288
60.0%
1 13096
 
20.0%
. 13096
 
20.0%

sensor_1
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size102.4 KiB
518.67
13096 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters78576
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row518.67
2nd row518.67
3rd row518.67
4th row518.67
5th row518.67

Common Values

ValueCountFrequency (%)
518.67 13096
100.0%

Length

2022-12-23T16:36:35.825563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-23T16:36:36.271828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
518.67 13096
100.0%

Most occurring characters

ValueCountFrequency (%)
5 13096
16.7%
1 13096
16.7%
8 13096
16.7%
. 13096
16.7%
6 13096
16.7%
7 13096
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 65480
83.3%
Other Punctuation 13096
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 13096
20.0%
1 13096
20.0%
8 13096
20.0%
6 13096
20.0%
7 13096
20.0%
Other Punctuation
ValueCountFrequency (%)
. 13096
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 78576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 13096
16.7%
1 13096
16.7%
8 13096
16.7%
. 13096
16.7%
6 13096
16.7%
7 13096
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 13096
16.7%
1 13096
16.7%
8 13096
16.7%
. 13096
16.7%
6 13096
16.7%
7 13096
16.7%

sensor_2
Real number (ℝ)

Distinct262
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean642.47509
Minimum641.13
Maximum644.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.4 KiB
2022-12-23T16:36:36.921937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum641.13
5-th percentile641.84
Q1642.1975
median642.46
Q3642.74
95-th percentile643.16
Maximum644.3
Range3.17
Interquartile range (IQR)0.5425

Descriptive statistics

Standard deviation0.40089934
Coefficient of variation (CV)0.00062399204
Kurtosis0.078362872
Mean642.47509
Median Absolute Deviation (MAD)0.27
Skewness0.22496247
Sum8413853.8
Variance0.16072028
MonotonicityNot monotonic
2022-12-23T16:36:37.210774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
642.49 179
 
1.4%
642.45 151
 
1.2%
642.54 149
 
1.1%
642.5 148
 
1.1%
642.34 142
 
1.1%
642.36 142
 
1.1%
642.43 136
 
1.0%
642.39 136
 
1.0%
642.67 135
 
1.0%
642.38 135
 
1.0%
Other values (252) 11643
88.9%
ValueCountFrequency (%)
641.13 1
 
< 0.1%
641.15 1
 
< 0.1%
641.26 1
 
< 0.1%
641.29 1
 
< 0.1%
641.3 2
< 0.1%
641.32 2
< 0.1%
641.34 4
< 0.1%
641.35 1
 
< 0.1%
641.37 1
 
< 0.1%
641.39 1
 
< 0.1%
ValueCountFrequency (%)
644.3 1
< 0.1%
644.07 1
< 0.1%
644.05 1
< 0.1%
644.04 1
< 0.1%
644.03 2
< 0.1%
643.93 1
< 0.1%
643.91 1
< 0.1%
643.89 1
< 0.1%
643.88 1
< 0.1%
643.86 2
< 0.1%

sensor_3
Real number (ℝ)

Distinct2361
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1588.0992
Minimum1569.04
Maximum1607.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.4 KiB
2022-12-23T16:36:38.155249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1569.04
5-th percentile1580.1
Q11584.6
median1587.99
Q31591.3625
95-th percentile1596.55
Maximum1607.55
Range38.51
Interquartile range (IQR)6.7625

Descriptive statistics

Standard deviation5.0032739
Coefficient of variation (CV)0.0031504795
Kurtosis0.060664027
Mean1588.0992
Median Absolute Deviation (MAD)3.385
Skewness0.15805855
Sum20797747
Variance25.03275
MonotonicityNot monotonic
2022-12-23T16:36:38.594625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1588.4 20
 
0.2%
1588.59 19
 
0.1%
1587.08 19
 
0.1%
1591.25 19
 
0.1%
1589.54 19
 
0.1%
1586.41 18
 
0.1%
1586.97 18
 
0.1%
1587.58 18
 
0.1%
1586.73 18
 
0.1%
1586.29 18
 
0.1%
Other values (2351) 12910
98.6%
ValueCountFrequency (%)
1569.04 1
< 0.1%
1570.12 1
< 0.1%
1571.02 1
< 0.1%
1571.13 1
< 0.1%
1572.37 1
< 0.1%
1572.84 1
< 0.1%
1572.91 1
< 0.1%
1573.06 1
< 0.1%
1573.19 1
< 0.1%
1573.23 1
< 0.1%
ValueCountFrequency (%)
1607.55 1
< 0.1%
1607.16 1
< 0.1%
1606.62 1
< 0.1%
1606.24 1
< 0.1%
1606.18 1
< 0.1%
1606.04 1
< 0.1%
1605.87 1
< 0.1%
1605.84 1
< 0.1%
1605.58 1
< 0.1%
1605.48 1
< 0.1%

sensor_4
Real number (ℝ)

Distinct2954
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1404.7354
Minimum1384.39
Maximum1433.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.4 KiB
2022-12-23T16:36:39.169141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1384.39
5-th percentile1394.46
Q11399.95
median1404.44
Q31409.05
95-th percentile1416.28
Maximum1433.36
Range48.97
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation6.6883093
Coefficient of variation (CV)0.0047612593
Kurtosis0.15466447
Mean1404.7354
Median Absolute Deviation (MAD)4.53
Skewness0.34210073
Sum18396414
Variance44.733481
MonotonicityNot monotonic
2022-12-23T16:36:39.473964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1402.53 17
 
0.1%
1404.09 16
 
0.1%
1404.77 16
 
0.1%
1407.6 15
 
0.1%
1403.1 15
 
0.1%
1400.89 15
 
0.1%
1403.93 15
 
0.1%
1406.96 15
 
0.1%
1400.01 14
 
0.1%
1400.95 14
 
0.1%
Other values (2944) 12944
98.8%
ValueCountFrequency (%)
1384.39 1
< 0.1%
1385.21 1
< 0.1%
1385.27 1
< 0.1%
1385.51 1
< 0.1%
1385.64 1
< 0.1%
1385.67 1
< 0.1%
1385.71 1
< 0.1%
1385.93 1
< 0.1%
1386.36 1
< 0.1%
1386.57 1
< 0.1%
ValueCountFrequency (%)
1433.36 1
< 0.1%
1432.95 1
< 0.1%
1432.29 1
< 0.1%
1430.85 1
< 0.1%
1430.64 1
< 0.1%
1429.89 1
< 0.1%
1429.85 1
< 0.1%
1429.66 1
< 0.1%
1429.31 1
< 0.1%
1429.15 1
< 0.1%

sensor_5
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size102.4 KiB
14.62
13096 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters65480
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row14.62
2nd row14.62
3rd row14.62
4th row14.62
5th row14.62

Common Values

ValueCountFrequency (%)
14.62 13096
100.0%

Length

2022-12-23T16:36:39.742813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-23T16:36:39.980369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
14.62 13096
100.0%

Most occurring characters

ValueCountFrequency (%)
1 13096
20.0%
4 13096
20.0%
. 13096
20.0%
6 13096
20.0%
2 13096
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52384
80.0%
Other Punctuation 13096
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13096
25.0%
4 13096
25.0%
6 13096
25.0%
2 13096
25.0%
Other Punctuation
ValueCountFrequency (%)
. 13096
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65480
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13096
20.0%
4 13096
20.0%
. 13096
20.0%
6 13096
20.0%
2 13096
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13096
20.0%
4 13096
20.0%
. 13096
20.0%
6 13096
20.0%
2 13096
20.0%

sensor_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size102.4 KiB
21.61
12704 
21.6
 
392

Length

Max length5
Median length5
Mean length4.9700672
Min length4

Characters and Unicode

Total characters65088
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21.61
2nd row21.61
3rd row21.61
4th row21.61
5th row21.61

Common Values

ValueCountFrequency (%)
21.61 12704
97.0%
21.6 392
 
3.0%

Length

2022-12-23T16:36:40.161283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-23T16:36:40.375353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
21.61 12704
97.0%
21.6 392
 
3.0%

Most occurring characters

ValueCountFrequency (%)
1 25800
39.6%
2 13096
20.1%
. 13096
20.1%
6 13096
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51992
79.9%
Other Punctuation 13096
 
20.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25800
49.6%
2 13096
25.2%
6 13096
25.2%
Other Punctuation
ValueCountFrequency (%)
. 13096
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65088
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 25800
39.6%
2 13096
20.1%
. 13096
20.1%
6 13096
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 25800
39.6%
2 13096
20.1%
. 13096
20.1%
6 13096
20.1%

sensor_7
Real number (ℝ)

Distinct415
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean553.75752
Minimum550.88
Maximum555.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.4 KiB
2022-12-23T16:36:40.578443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum550.88
5-th percentile552.58
Q1553.31
median553.8
Q3554.24
95-th percentile554.8
Maximum555.84
Range4.96
Interquartile range (IQR)0.93

Descriptive statistics

Standard deviation0.68128611
Coefficient of variation (CV)0.0012302968
Kurtosis0.11207596
Mean553.75752
Median Absolute Deviation (MAD)0.46
Skewness-0.34570116
Sum7252008.5
Variance0.46415076
MonotonicityNot monotonic
2022-12-23T16:36:40.890922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
554.11 90
 
0.7%
553.81 88
 
0.7%
553.82 87
 
0.7%
554.02 86
 
0.7%
553.78 86
 
0.7%
553.93 83
 
0.6%
553.65 83
 
0.6%
553.79 83
 
0.6%
554.07 83
 
0.6%
553.8 82
 
0.6%
Other values (405) 12245
93.5%
ValueCountFrequency (%)
550.88 1
< 0.1%
550.91 1
< 0.1%
550.94 1
< 0.1%
551.01 1
< 0.1%
551.15 1
< 0.1%
551.19 2
< 0.1%
551.21 1
< 0.1%
551.23 1
< 0.1%
551.26 1
< 0.1%
551.34 2
< 0.1%
ValueCountFrequency (%)
555.84 1
< 0.1%
555.81 1
< 0.1%
555.8 1
< 0.1%
555.72 1
< 0.1%
555.69 1
< 0.1%
555.65 1
< 0.1%
555.64 1
< 0.1%
555.63 1
< 0.1%
555.61 1
< 0.1%
555.57 1
< 0.1%

sensor_8
Real number (ℝ)

Distinct41
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2388.071
Minimum2387.89
Maximum2388.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.4 KiB
2022-12-23T16:36:41.172174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2387.89
5-th percentile2387.98
Q12388.03
median2388.07
Q32388.11
95-th percentile2388.17
Maximum2388.3
Range0.41
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.057441784
Coefficient of variation (CV)2.4053634 × 10-5
Kurtosis-0.062196886
Mean2388.071
Median Absolute Deviation (MAD)0.04
Skewness0.30340178
Sum31274177
Variance0.0032995586
MonotonicityNot monotonic
2022-12-23T16:36:41.469014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
2388.05 883
 
6.7%
2388.07 863
 
6.6%
2388.04 852
 
6.5%
2388.06 821
 
6.3%
2388.08 813
 
6.2%
2388.09 772
 
5.9%
2388.03 763
 
5.8%
2388.02 756
 
5.8%
2388.1 728
 
5.6%
2388.11 685
 
5.2%
Other values (31) 5160
39.4%
ValueCountFrequency (%)
2387.89 1
 
< 0.1%
2387.91 8
 
0.1%
2387.92 6
 
< 0.1%
2387.93 20
 
0.2%
2387.94 33
 
0.3%
2387.95 64
 
0.5%
2387.96 110
 
0.8%
2387.97 187
1.4%
2387.98 296
2.3%
2387.99 371
2.8%
ValueCountFrequency (%)
2388.3 1
 
< 0.1%
2388.29 5
 
< 0.1%
2388.28 3
 
< 0.1%
2388.27 5
 
< 0.1%
2388.26 13
 
0.1%
2388.25 11
 
0.1%
2388.24 22
 
0.2%
2388.23 37
0.3%
2388.22 31
0.2%
2388.21 56
0.4%

sensor_9
Real number (ℝ)

Distinct4047
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9058.4074
Minimum9024.53
Maximum9155.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.4 KiB
2022-12-23T16:36:41.940427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9024.53
5-th percentile9043.09
Q19051.02
median9057.32
Q39064.11
95-th percentile9076.2725
Maximum9155.03
Range130.5
Interquartile range (IQR)13.09

Descriptive statistics

Standard deviation11.436261
Coefficient of variation (CV)0.0012625023
Kurtosis7.5258067
Mean9058.4074
Median Absolute Deviation (MAD)6.56
Skewness1.6547139
Sum1.186289 × 108
Variance130.78805
MonotonicityNot monotonic
2022-12-23T16:36:42.724974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9059.45 16
 
0.1%
9062.01 14
 
0.1%
9060.38 13
 
0.1%
9052.93 13
 
0.1%
9054.49 13
 
0.1%
9056.96 12
 
0.1%
9058.17 12
 
0.1%
9055.84 12
 
0.1%
9059.46 12
 
0.1%
9055.07 12
 
0.1%
Other values (4037) 12967
99.0%
ValueCountFrequency (%)
9024.53 1
< 0.1%
9025 1
< 0.1%
9025.97 1
< 0.1%
9026.89 1
< 0.1%
9029.71 1
< 0.1%
9029.72 1
< 0.1%
9029.81 1
< 0.1%
9030.68 1
< 0.1%
9030.8 1
< 0.1%
9030.97 1
< 0.1%
ValueCountFrequency (%)
9155.03 1
< 0.1%
9148.85 1
< 0.1%
9148.56 1
< 0.1%
9146.81 1
< 0.1%
9146.03 1
< 0.1%
9145.88 1
< 0.1%
9145.77 1
< 0.1%
9142.37 1
< 0.1%
9142.18 1
< 0.1%
9141.92 1
< 0.1%

sensor_10
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size102.4 KiB
1.3
13096 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters39288
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.3
2nd row1.3
3rd row1.3
4th row1.3
5th row1.3

Common Values

ValueCountFrequency (%)
1.3 13096
100.0%

Length

2022-12-23T16:36:43.069776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-23T16:36:43.450149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.3 13096
100.0%

Most occurring characters

ValueCountFrequency (%)
1 13096
33.3%
. 13096
33.3%
3 13096
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26192
66.7%
Other Punctuation 13096
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13096
50.0%
3 13096
50.0%
Other Punctuation
ValueCountFrequency (%)
. 13096
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13096
33.3%
. 13096
33.3%
3 13096
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13096
33.3%
. 13096
33.3%
3 13096
33.3%

sensor_11
Real number (ℝ)

Distinct136
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.416204
Minimum46.8
Maximum48.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.4 KiB
2022-12-23T16:36:43.762629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum46.8
5-th percentile47.12
Q147.27
median47.41
Q347.54
95-th percentile47.75
Maximum48.26
Range1.46
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation0.19591725
Coefficient of variation (CV)0.0041318627
Kurtosis0.22072902
Mean47.416204
Median Absolute Deviation (MAD)0.13
Skewness0.40444895
Sum620962.61
Variance0.038383567
MonotonicityNot monotonic
2022-12-23T16:36:44.130674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.41 296
 
2.3%
47.43 287
 
2.2%
47.32 273
 
2.1%
47.42 270
 
2.1%
47.35 269
 
2.1%
47.33 265
 
2.0%
47.36 261
 
2.0%
47.37 261
 
2.0%
47.5 255
 
1.9%
47.38 251
 
1.9%
Other values (126) 10408
79.5%
ValueCountFrequency (%)
46.8 1
 
< 0.1%
46.84 1
 
< 0.1%
46.86 2
 
< 0.1%
46.87 1
 
< 0.1%
46.89 3
 
< 0.1%
46.9 2
 
< 0.1%
46.91 3
 
< 0.1%
46.92 3
 
< 0.1%
46.93 8
0.1%
46.94 5
< 0.1%
ValueCountFrequency (%)
48.26 1
 
< 0.1%
48.23 1
 
< 0.1%
48.2 1
 
< 0.1%
48.18 2
< 0.1%
48.16 1
 
< 0.1%
48.15 2
< 0.1%
48.14 1
 
< 0.1%
48.13 3
< 0.1%
48.12 2
< 0.1%
48.11 3
< 0.1%

sensor_12
Real number (ℝ)

Distinct357
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521.74772
Minimum519.38
Maximum523.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.4 KiB
2022-12-23T16:36:44.568143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum519.38
5-th percentile520.78
Q1521.38
median521.78
Q3522.15
95-th percentile522.59
Maximum523.76
Range4.38
Interquartile range (IQR)0.77

Descriptive statistics

Standard deviation0.55962675
Coefficient of variation (CV)0.0010726003
Kurtosis0.22539176
Mean521.74772
Median Absolute Deviation (MAD)0.38
Skewness-0.38153086
Sum6832808.2
Variance0.3131821
MonotonicityNot monotonic
2022-12-23T16:36:44.896247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521.9 112
 
0.9%
521.78 110
 
0.8%
521.93 107
 
0.8%
522.02 106
 
0.8%
521.69 105
 
0.8%
521.71 104
 
0.8%
521.85 104
 
0.8%
521.74 103
 
0.8%
522.17 103
 
0.8%
522.13 102
 
0.8%
Other values (347) 12040
91.9%
ValueCountFrequency (%)
519.38 1
< 0.1%
519.39 1
< 0.1%
519.44 1
< 0.1%
519.55 2
< 0.1%
519.58 1
< 0.1%
519.6 1
< 0.1%
519.66 1
< 0.1%
519.67 1
< 0.1%
519.68 1
< 0.1%
519.71 1
< 0.1%
ValueCountFrequency (%)
523.76 1
< 0.1%
523.44 1
< 0.1%
523.42 1
< 0.1%
523.37 1
< 0.1%
523.33 2
< 0.1%
523.29 1
< 0.1%
523.28 1
< 0.1%
523.21 1
< 0.1%
523.2 2
< 0.1%
523.18 2
< 0.1%

sensor_13
Real number (ℝ)

Distinct43
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2388.071
Minimum2387.89
Maximum2388.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.4 KiB
2022-12-23T16:36:45.193101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2387.89
5-th percentile2387.98
Q12388.03
median2388.07
Q32388.11
95-th percentile2388.17
Maximum2388.32
Range0.43
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.05693431
Coefficient of variation (CV)2.3841129 × 10-5
Kurtosis-0.028471718
Mean2388.071
Median Absolute Deviation (MAD)0.04
Skewness0.29053324
Sum31274178
Variance0.0032415157
MonotonicityNot monotonic
2022-12-23T16:36:45.458708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
2388.07 909
 
6.9%
2388.06 886
 
6.8%
2388.04 848
 
6.5%
2388.05 845
 
6.5%
2388.03 812
 
6.2%
2388.08 783
 
6.0%
2388.09 773
 
5.9%
2388.11 754
 
5.8%
2388.02 749
 
5.7%
2388.1 735
 
5.6%
Other values (33) 5002
38.2%
ValueCountFrequency (%)
2387.89 3
 
< 0.1%
2387.9 1
 
< 0.1%
2387.91 1
 
< 0.1%
2387.92 8
 
0.1%
2387.93 24
 
0.2%
2387.94 37
 
0.3%
2387.95 69
 
0.5%
2387.96 108
 
0.8%
2387.97 171
1.3%
2387.98 292
2.2%
ValueCountFrequency (%)
2388.32 2
 
< 0.1%
2388.3 1
 
< 0.1%
2388.29 1
 
< 0.1%
2388.28 2
 
< 0.1%
2388.27 7
 
0.1%
2388.26 8
 
0.1%
2388.25 13
 
0.1%
2388.24 16
 
0.1%
2388.23 33
0.3%
2388.22 44
0.3%

sensor_14
Real number (ℝ)

Distinct3786
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8138.9478
Minimum8108.5
Maximum8220.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.4 KiB
2022-12-23T16:36:46.146161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8108.5
5-th percentile8124.56
Q18132.31
median8138.39
Q38144.36
95-th percentile8154.075
Maximum8220.48
Range111.98
Interquartile range (IQR)12.05

Descriptive statistics

Standard deviation10.188605
Coefficient of variation (CV)0.0012518332
Kurtosis6.3030939
Mean8138.9478
Median Absolute Deviation (MAD)6.02
Skewness1.3576338
Sum1.0658766 × 108
Variance103.80767
MonotonicityNot monotonic
2022-12-23T16:36:46.427392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8141.99 15
 
0.1%
8133.28 13
 
0.1%
8140.51 13
 
0.1%
8138.31 13
 
0.1%
8141.03 12
 
0.1%
8138.96 12
 
0.1%
8145.08 12
 
0.1%
8133.1 12
 
0.1%
8137.43 12
 
0.1%
8132.98 12
 
0.1%
Other values (3776) 12970
99.0%
ValueCountFrequency (%)
8108.5 1
< 0.1%
8109.03 1
< 0.1%
8111.16 1
< 0.1%
8111.3 1
< 0.1%
8111.8 1
< 0.1%
8112.19 1
< 0.1%
8112.45 1
< 0.1%
8112.58 1
< 0.1%
8112.64 1
< 0.1%
8113.02 1
< 0.1%
ValueCountFrequency (%)
8220.48 1
< 0.1%
8218.13 1
< 0.1%
8217.24 1
< 0.1%
8214.64 1
< 0.1%
8214.33 1
< 0.1%
8213.57 1
< 0.1%
8213.28 1
< 0.1%
8211.53 1
< 0.1%
8211.21 1
< 0.1%
8210.85 1
< 0.1%

sensor_15
Real number (ℝ)

Distinct1506
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.425844
Minimum8.3328
Maximum8.5414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.4 KiB
2022-12-23T16:36:46.802346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8.3328
5-th percentile8.3801
Q18.4056
median8.4249
Q38.4443
95-th percentile8.475925
Maximum8.5414
Range0.2086
Interquartile range (IQR)0.0387

Descriptive statistics

Standard deviation0.029009328
Coefficient of variation (CV)0.0034428987
Kurtosis0.11255642
Mean8.425844
Median Absolute Deviation (MAD)0.0194
Skewness0.26555184
Sum110344.85
Variance0.00084154108
MonotonicityNot monotonic
2022-12-23T16:36:47.083597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.4223 30
 
0.2%
8.4347 29
 
0.2%
8.4247 28
 
0.2%
8.4282 27
 
0.2%
8.412 26
 
0.2%
8.4344 26
 
0.2%
8.4098 25
 
0.2%
8.4054 25
 
0.2%
8.4313 25
 
0.2%
8.4255 25
 
0.2%
Other values (1496) 12830
98.0%
ValueCountFrequency (%)
8.3328 1
< 0.1%
8.333 1
< 0.1%
8.3332 1
< 0.1%
8.3359 1
< 0.1%
8.3392 1
< 0.1%
8.3414 1
< 0.1%
8.3417 1
< 0.1%
8.3445 1
< 0.1%
8.3447 1
< 0.1%
8.3451 2
< 0.1%
ValueCountFrequency (%)
8.5414 1
< 0.1%
8.5375 1
< 0.1%
8.5374 1
< 0.1%
8.5359 1
< 0.1%
8.5354 1
< 0.1%
8.5343 1
< 0.1%
8.534 1
< 0.1%
8.5294 1
< 0.1%
8.5293 1
< 0.1%
8.5278 1
< 0.1%

sensor_16
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size102.4 KiB
0.03
13096 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters52384
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.03
2nd row0.03
3rd row0.03
4th row0.03
5th row0.03

Common Values

ValueCountFrequency (%)
0.03 13096
100.0%

Length

2022-12-23T16:36:47.349205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-23T16:36:47.692932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03 13096
100.0%

Most occurring characters

ValueCountFrequency (%)
0 26192
50.0%
. 13096
25.0%
3 13096
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39288
75.0%
Other Punctuation 13096
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26192
66.7%
3 13096
33.3%
Other Punctuation
ValueCountFrequency (%)
. 13096
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 52384
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26192
50.0%
. 13096
25.0%
3 13096
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26192
50.0%
. 13096
25.0%
3 13096
25.0%

sensor_17
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean392.57162
Minimum389
Maximum397
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.4 KiB
2022-12-23T16:36:47.849171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum389
5-th percentile391
Q1392
median393
Q3393
95-th percentile395
Maximum397
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2335768
Coefficient of variation (CV)0.0031422975
Kurtosis0.10779458
Mean392.57162
Median Absolute Deviation (MAD)1
Skewness0.21669889
Sum5141118
Variance1.5217118
MonotonicityNot monotonic
2022-12-23T16:36:48.208521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
392 3962
30.3%
393 3911
29.9%
391 2003
15.3%
394 1981
15.1%
395 642
 
4.9%
390 412
 
3.1%
396 123
 
0.9%
389 36
 
0.3%
397 26
 
0.2%
ValueCountFrequency (%)
389 36
 
0.3%
390 412
 
3.1%
391 2003
15.3%
392 3962
30.3%
393 3911
29.9%
394 1981
15.1%
395 642
 
4.9%
396 123
 
0.9%
397 26
 
0.2%
ValueCountFrequency (%)
397 26
 
0.2%
396 123
 
0.9%
395 642
 
4.9%
394 1981
15.1%
393 3911
29.9%
392 3962
30.3%
391 2003
15.3%
390 412
 
3.1%
389 36
 
0.3%

sensor_18
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size102.4 KiB
2388
13096 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters52384
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2388
2nd row2388
3rd row2388
4th row2388
5th row2388

Common Values

ValueCountFrequency (%)
2388 13096
100.0%

Length

2022-12-23T16:36:48.442881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-23T16:36:48.645992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2388 13096
100.0%

Most occurring characters

ValueCountFrequency (%)
8 26192
50.0%
2 13096
25.0%
3 13096
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52384
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 26192
50.0%
2 13096
25.0%
3 13096
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 52384
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 26192
50.0%
2 13096
25.0%
3 13096
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 26192
50.0%
2 13096
25.0%
3 13096
25.0%

sensor_19
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size102.4 KiB
100.0
13096 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters65480
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row100.0
4th row100.0
5th row100.0

Common Values

ValueCountFrequency (%)
100.0 13096
100.0%

Length

2022-12-23T16:36:48.802232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-23T16:36:49.067838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
100.0 13096
100.0%

Most occurring characters

ValueCountFrequency (%)
0 39288
60.0%
1 13096
 
20.0%
. 13096
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52384
80.0%
Other Punctuation 13096
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39288
75.0%
1 13096
 
25.0%
Other Punctuation
ValueCountFrequency (%)
. 13096
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65480
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39288
60.0%
1 13096
 
20.0%
. 13096
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39288
60.0%
1 13096
 
20.0%
. 13096
 
20.0%

sensor_20
Real number (ℝ)

Distinct103
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.892502
Minimum38.31
Maximum39.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.4 KiB
2022-12-23T16:36:49.505291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum38.31
5-th percentile38.65
Q138.8
median38.9
Q338.99
95-th percentile39.12
Maximum39.41
Range1.1
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.14168076
Coefficient of variation (CV)0.003642881
Kurtosis0.19524364
Mean38.892502
Median Absolute Deviation (MAD)0.09
Skewness-0.23325231
Sum509336.2
Variance0.020073436
MonotonicityNot monotonic
2022-12-23T16:36:49.786539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.9 385
 
2.9%
38.89 379
 
2.9%
38.95 377
 
2.9%
38.88 375
 
2.9%
38.91 374
 
2.9%
38.92 374
 
2.9%
38.97 364
 
2.8%
38.93 360
 
2.7%
38.94 359
 
2.7%
38.96 358
 
2.7%
Other values (93) 9391
71.7%
ValueCountFrequency (%)
38.31 1
 
< 0.1%
38.33 1
 
< 0.1%
38.35 4
< 0.1%
38.36 2
 
< 0.1%
38.37 3
< 0.1%
38.38 1
 
< 0.1%
38.39 2
 
< 0.1%
38.41 1
 
< 0.1%
38.42 5
< 0.1%
38.43 3
< 0.1%
ValueCountFrequency (%)
39.41 1
 
< 0.1%
39.4 1
 
< 0.1%
39.36 3
< 0.1%
39.34 1
 
< 0.1%
39.33 2
 
< 0.1%
39.31 4
< 0.1%
39.3 3
< 0.1%
39.29 5
< 0.1%
39.28 7
0.1%
39.27 5
< 0.1%

sensor_21
Real number (ℝ)

Distinct3555
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.335743
Minimum22.9354
Maximum23.6419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.4 KiB
2022-12-23T16:36:50.677104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum22.9354
5-th percentile23.191275
Q123.2816
median23.3392
Q323.3936
95-th percentile23.4681
Maximum23.6419
Range0.7065
Interquartile range (IQR)0.112

Descriptive statistics

Standard deviation0.08412028
Coefficient of variation (CV)0.0036047826
Kurtosis0.15330738
Mean23.335743
Median Absolute Deviation (MAD)0.0558
Skewness-0.24723334
Sum305604.89
Variance0.0070762215
MonotonicityNot monotonic
2022-12-23T16:36:50.942712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.3518 15
 
0.1%
23.3756 15
 
0.1%
23.3727 13
 
0.1%
23.3662 13
 
0.1%
23.3349 13
 
0.1%
23.3075 13
 
0.1%
23.3472 13
 
0.1%
23.3785 12
 
0.1%
23.3931 12
 
0.1%
23.4031 12
 
0.1%
Other values (3545) 12965
99.0%
ValueCountFrequency (%)
22.9354 1
< 0.1%
22.985 1
< 0.1%
23.0071 1
< 0.1%
23.0104 1
< 0.1%
23.0121 1
< 0.1%
23.02 1
< 0.1%
23.0242 1
< 0.1%
23.0345 1
< 0.1%
23.0349 1
< 0.1%
23.0403 1
< 0.1%
ValueCountFrequency (%)
23.6419 1
< 0.1%
23.6229 1
< 0.1%
23.6021 1
< 0.1%
23.6003 1
< 0.1%
23.5863 1
< 0.1%
23.5852 1
< 0.1%
23.5788 1
< 0.1%
23.577 1
< 0.1%
23.5749 1
< 0.1%
23.5707 1
< 0.1%

Interactions

2022-12-23T16:36:08.039716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:32.032333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:39.103296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:44.393275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:49.378628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:54.565665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:59.691686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:06.446825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:11.179121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:16.821251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:21.498562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:26.851438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:33.178827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:38.039051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:42.628430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:47.775310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:54.806239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:36:00.758962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:36:08.305327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:32.378134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:39.682961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:44.654324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:49.634478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:54.864697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:59.970544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:06.684688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:11.439304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:17.057095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:21.792394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:27.105293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:33.410693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:38.275916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:42.875285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:48.019569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:55.306207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:36:00.977697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:36:08.555327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:32.810887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:40.209663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:44.924190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:49.900331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:55.148531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:00.530203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:06.949539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:11.762122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:17.297958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:22.050264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:27.368143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:33.687535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:38.532769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:43.553232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:48.259813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:55.556189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:36:01.227683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:36:08.820933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:33.199665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:40.664422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:45.268975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:50.226143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:55.450361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:01.166839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:07.225380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:12.071945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:17.569803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:22.373063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:27.653001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:33.964375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:38.810608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:43.848057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:48.541041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:55.853049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:36:01.508911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:36:09.070895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:33.586443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:40.916278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:45.543816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:50.600926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:55.723224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:01.522637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:07.477235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:12.347786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:17.824657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:22.646903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:27.994789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:34.209235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:39.058466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:44.117899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:48.791044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:56.118672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:36:01.758904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:36:09.320884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:33.988214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:41.167118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:45.850644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:50.887767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:55.988056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:01.789488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:07.737087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:12.615632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:18.071517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:23.246498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-12-23T16:35:37.400412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:42.115721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:47.281257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:53.634448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:36:00.243373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:36:07.477274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:36:13.877965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:38.699544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:44.145435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:49.106802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:54.272833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:34:59.422838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:05.219525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:10.871300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:16.566377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:21.126773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:26.584611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:32.901985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:37.752211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:42.366576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:47.525347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:35:54.087539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:36:00.493352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:36:07.711612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-12-23T16:36:52.614472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-23T16:36:53.520661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-23T16:36:54.708080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-23T16:36:55.520525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-23T16:36:56.176713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-23T16:36:15.602657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-23T16:36:17.341665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unit_numbertime_cyclesOS_1OS_2OS_3sensor_1sensor_2sensor_3sensor_4sensor_5sensor_6sensor_7sensor_8sensor_9sensor_10sensor_11sensor_12sensor_13sensor_14sensor_15sensor_16sensor_17sensor_18sensor_19sensor_20sensor_21
0110.00230.0003100.0518.67643.021585.291398.2114.6221.61553.902388.049050.171.347.20521.722388.038125.558.40520.033922388100.038.8623.3735
112-0.0027-0.0003100.0518.67641.711588.451395.4214.6221.61554.852388.019054.421.347.50522.162388.068139.628.38030.033932388100.039.0223.3916
2130.00030.0001100.0518.67642.461586.941401.3414.6221.61554.112388.059056.961.347.50521.972388.038130.108.44410.033932388100.039.0823.4166
3140.00420.0000100.0518.67642.441584.121406.4214.6221.61554.072388.039045.291.347.28521.382388.058132.908.39170.033912388100.039.0023.3737
4150.00140.0000100.0518.67642.511587.191401.9214.6221.61554.162388.019044.551.347.31522.152388.038129.548.40310.033902388100.038.9923.4130
5160.00120.0003100.0518.67642.111579.121395.1314.6221.61554.222388.009050.961.347.26521.922388.088127.468.42380.033922388100.038.9123.3467
617-0.00000.0002100.0518.67642.111583.341404.8414.6221.61553.892388.059051.391.347.31522.012388.068134.978.39140.033912388100.038.8523.3952
7180.0006-0.0000100.0518.67642.541580.891400.8914.6221.61553.592388.059052.861.347.21522.092388.068125.938.42130.033932388100.039.0523.3224
819-0.00360.0000100.0518.67641.881593.291412.2814.6221.61554.492388.069048.551.347.37522.032388.058134.158.43530.033912388100.039.1023.4521
9110-0.0025-0.0001100.0518.67642.071585.251398.6414.6221.61554.282388.049051.951.347.14522.002388.068134.088.40930.033912388100.038.8723.3820
Unit_numbertime_cyclesOS_1OS_2OS_3sensor_1sensor_2sensor_3sensor_4sensor_5sensor_6sensor_7sensor_8sensor_9sensor_10sensor_11sensor_12sensor_13sensor_14sensor_15sensor_16sensor_17sensor_18sensor_19sensor_20sensor_21
13086100189-0.00030.0002100.0518.67643.291592.331417.6614.6221.61553.592388.069136.551.347.55520.932388.098209.848.44230.033932388100.038.7123.3188
13087100190-0.00380.0002100.0518.67642.951598.971421.2814.6221.61553.592388.109137.351.347.49521.632388.078207.958.47650.033952388100.038.7423.3551
13088100191-0.0031-0.0001100.0518.67642.921589.541413.6514.6221.61553.242388.029136.191.347.61521.232388.078201.948.48770.033962388100.038.8923.2279
13089100192-0.00340.0001100.0518.67643.051598.181418.5814.6221.61553.162388.059141.921.347.57520.992388.078210.248.41710.033952388100.038.7723.2148
130901001930.00180.0004100.0518.67643.101595.601414.6214.6221.61553.182388.089139.881.347.58521.372388.058213.578.44290.033952388100.038.6323.2952
130911001940.00490.0000100.0518.67643.241599.451415.7914.6221.61553.412388.029142.371.347.69520.692388.008213.288.47150.033942388100.038.6523.1974
13092100195-0.0011-0.0001100.0518.67643.221595.691422.0514.6221.61553.222388.059140.681.347.60521.052388.098210.858.45120.033952388100.038.5723.2771
13093100196-0.0006-0.0003100.0518.67643.441593.151406.8214.6221.61553.042388.119146.811.347.57521.182388.048217.248.45690.033952388100.038.6223.2051
13094100197-0.00380.0001100.0518.67643.261594.991419.3614.6221.61553.372388.079148.851.347.61521.332388.088220.488.47110.033952388100.038.6623.2699
130951001980.00130.0003100.0518.67642.951601.621424.9914.6221.61552.482388.069155.031.347.80521.072388.058214.648.49030.033962388100.038.7023.1855